Distributed Agent-Based Simulation and GIS: An Experiment with the Dynamics of Social Norms

In the last decade, the investigation of the social complexity has witnessed the rise of Computational Social Science, a research paradigm that heavily relies upon data and computation to foster our understanding of social phenomena. In this field, a key role is played by the explanatory and predictive power of agent-based social simulations that are showing to take advantage of GIS, higher number of agents and real data. We focus GIS based distibuted ABMs. We observed that the density distribution of agents, over the field, strongly impact on the overall performances. In order to better understand this issue, we analyzes three different scenarios ranging from real positioning, where the citizens are positioned according to a real dataset to a random positioning where the agent are positioned uniformly at random on the field. Results confirm our hypothesis and show that an irregular distribution of the agents over the field increases the communication overhead. We provide also an analytic analysis which, in a 2-dimensional uniform field partitioning, is affected by several parameters (which depend on the model), but is also influenced by the density distribution of agents over the field. According to the presented results, we have that uniform space partitioning strategy does not scale on GIS based ABM characterized by an irregular distribution of agents.